Tweet
Hey,
Since I couldn't find anything with the search function, I took the freedom to ask:
Is there an EFS which calculates the "beta" between 2 stocks over a given timeperiod?
Usually the beta is:
Beta measures stock price volatility relative to the overall stock market. We use the S&P 500 as a proxy for the market and we automatically define its Beta as being 1.00. A higher beta indicates that a stock is relatively volatile while a lower beta indicates more stability. A stock with a Beta of 0.90 would, on average, be expected to rise or fall only 90% as much as the market. So if the market dropped 1.0%, such a stock might rise or fall 0.9% On the other hand, a stock with a Beta of 1.10 would, on average, rise or fall 10% more than the market. So a 1.0% market move, up or down, should spur a 1.1% move for the stock.
Since I do know that beta is the slope of a linear regression trendline I would like to calculate it by:
Beta = ( covariance(stock1stock2) ) / variance (stock1)
Thanks for any suggestions,
Ptrading.
Since I couldn't find anything with the search function, I took the freedom to ask:
Is there an EFS which calculates the "beta" between 2 stocks over a given timeperiod?
Usually the beta is:
Beta measures stock price volatility relative to the overall stock market. We use the S&P 500 as a proxy for the market and we automatically define its Beta as being 1.00. A higher beta indicates that a stock is relatively volatile while a lower beta indicates more stability. A stock with a Beta of 0.90 would, on average, be expected to rise or fall only 90% as much as the market. So if the market dropped 1.0%, such a stock might rise or fall 0.9% On the other hand, a stock with a Beta of 1.10 would, on average, rise or fall 10% more than the market. So a 1.0% market move, up or down, should spur a 1.1% move for the stock.
Since I do know that beta is the slope of a linear regression trendline I would like to calculate it by:
Beta = ( covariance(stock1stock2) ) / variance (stock1)
Thanks for any suggestions,
Ptrading.